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Cloudeval AI
Your cloud evaluated, reported, and agent-ready in CLI & Web
CloudEval AI reviews Azure infrastructure from ARM/Bicep, live Azure environment, CLI, and GitHub Actions. Generate architecture diagrams, cost reports, Well-Architected findings, and grounded AI answers backed by evidence — before changes hit production.
I built CloudEval AI because cloud architecture reviews are still too manual. Teams jump between ARM/Bicep, cloud portals, cost dashboards, Well-Architected docs, diagrams, and PR reviews just to answer one question: is this infrastructure safe to ship?
CloudEval reviews Azure infrastructure (AWS/GCP soon) from the web app, CLI, and GitHub Actions (CI/CD). You can import ARM/Bicep or connect live Azure, then generate architecture diagrams, cost reports, Well-Architected findings, and grounded AI answers backed by evidence.
The goal is not “AI magic.” The goal is a cloud review workflow engineers can actually trust: source-aware, evidence-backed, and usable before changes hit production.
I’m looking for blunt feedback from Azure engineers, DevOps teams, platform engineers, cloud architects, and FinOps/security folks.
Specific questions:
Would you use this more in the browser, CLI, or GitHub Actions?
What would make you trust an AI-generated cloud finding?
Should CloudEval stay Azure-first for now, or is AWS/GCP required immediately?
What cloud review task do you still do manually today?
I’m not looking for polite praise. I want to know where this breaks, what feels unclear, and what would make you actually use it in your workflow.
Is there a way to offset the pricing with internal discounts some organizations get?
When the cost report is generated is it based on current Azure pricing at that moment, or is there a risk the estimates go stale if prices change?
About Cloudeval AI on Product Hunt
“Your cloud evaluated, reported, and agent-ready in CLI & Web”
Cloudeval AI was submitted on Product Hunt and earned 18 upvotes and 12 comments, placing #34 on the daily leaderboard. CloudEval AI reviews Azure infrastructure from ARM/Bicep, live Azure environment, CLI, and GitHub Actions. Generate architecture diagrams, cost reports, Well-Architected findings, and grounded AI answers backed by evidence — before changes hit production.
Cloudeval AI was featured in Productivity (655.7k followers), SaaS (43k followers), Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 398.4k products, making this a competitive space to launch in.
Who hunted Cloudeval AI?
Cloudeval AI was hunted by Prateek Singh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Cloudeval AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Prateek Singh
Hey Product Hunt, I’m Prateek, founder of Ganak AI Labs.
I built CloudEval AI because cloud architecture reviews are still too manual. Teams jump between ARM/Bicep, cloud portals, cost dashboards, Well-Architected docs, diagrams, and PR reviews just to answer one question: is this infrastructure safe to ship?
CloudEval reviews Azure infrastructure (AWS/GCP soon) from the web app, CLI, and GitHub Actions (CI/CD). You can import ARM/Bicep or connect live Azure, then generate architecture diagrams, cost reports, Well-Architected findings, and grounded AI answers backed by evidence.
The goal is not “AI magic.” The goal is a cloud review workflow engineers can actually trust: source-aware, evidence-backed, and usable before changes hit production.
I’m looking for blunt feedback from Azure engineers, DevOps teams, platform engineers, cloud architects, and FinOps/security folks.
Specific questions:
Would you use this more in the browser, CLI, or GitHub Actions?
What would make you trust an AI-generated cloud finding?
Should CloudEval stay Azure-first for now, or is AWS/GCP required immediately?
What cloud review task do you still do manually today?
I’m not looking for polite praise. I want to know where this breaks, what feels unclear, and what would make you actually use it in your workflow.